论文标题
天际线：深度神经网络培训的交互式内部计算性能分析
Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
论文作者
论文摘要
培训最先进的深度神经网络（DNN）是一个计算且耗时的过程，它激励深度学习开发人员调试其DNN以进行计算性能。但是，有效执行此调试需要有关基础软件和硬件系统的深入了解，这是典型的深度学习开发人员可能没有的。为了帮助弥合这一差距，我们提出了Skyline：一种用于DNN培训的新交互式工具，该工具支持编辑内部计算性能分析，可视化和调试。 Skyline的主要贡献是，它利用DNN培训的特殊计算属性来提供（i）交互式性能预测和可视化，以及（ii）直接操纵可视化的可视化，这些可视化效果在拖动时，将代码中的批处理大小突变。作为编辑工具，Skyline允许用户利用这些诊断功能在开发过程中调试其DNN的性能。天际线的探索性定性用户研究产生了有希望的结果；所有参与者都认为天际线很有用且易于使用。
Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance. However, effectively performing this debugging requires intimate knowledge about the underlying software and hardware systems---something that the typical deep learning developer may not have. To help bridge this gap, we present Skyline: a new interactive tool for DNN training that supports in-editor computational performance profiling, visualization, and debugging. Skyline's key contribution is that it leverages special computational properties of DNN training to provide (i) interactive performance predictions and visualizations, and (ii) directly manipulatable visualizations that, when dragged, mutate the batch size in the code. As an in-editor tool, Skyline allows users to leverage these diagnostic features to debug the performance of their DNNs during development. An exploratory qualitative user study of Skyline produced promising results; all the participants found Skyline to be useful and easy to use.
